<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rastkar, Sarah</style></author><author><style face="normal" font="default" size="100%">Murphy, Gail C.</style></author><author><style face="normal" font="default" size="100%">Murray, Gabriel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Summarizing software artifacts: a case study of bug reports</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">ICSE '10</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bug reports</style></keyword><keyword><style  face="normal" font="default" size="100%">eclipse</style></keyword><keyword><style  face="normal" font="default" size="100%">gnome</style></keyword><keyword><style  face="normal" font="default" size="100%">human-centric software engineering</style></keyword><keyword><style  face="normal" font="default" size="100%">kde</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">mozilla</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://doi.acm.org/10.1145/1806799.1806872</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">New York, NY, USA</style></pub-location><pages><style face="normal" font="default" size="100%">505–514</style></pages><isbn><style face="normal" font="default" size="100%">978-1-60558-719-6</style></isbn><abstract><style face="normal" font="default" size="100%">Many software artifacts are created, maintained and evolved as part of a software development project. As software developers work on a project, they interact with existing project artifacts, performing such activities as reading previously filed bug reports in search of duplicate reports. These activities often require a developer to peruse a substantial amount of text. In this paper, we investigate whether it is possible to summarize software artifacts automatically and effectively so that developers could consult smaller summaries instead of entire artifacts. To provide focus to our investigation, we consider the generation of summaries for bug reports. We found that existing conversation-based generators can produce better results than random generators and that a generator trained specifically on bug reports can perform statistically better than existing conversation-based generators. We demonstrate that humans also find these generated summaries reasonable indicating that summaries might be used effectively for many tasks.</style></abstract></record></records></xml>